Provides introductions and tutorials on Bilibili-related technologies.
This article explains what an AI Agent is, distinguishes it from traditional workflows, and demonstrates how to build and customize agents using the Go‑based Blades framework, covering core concepts, code examples, five workflow patterns, best‑practice guidelines, and reference resources.
This article analyzes the current bottlenecks of Bilibili's private messaging service, explains the technical challenges of massive data volume and traffic spikes, and presents a comprehensive multi‑layer architecture upgrade—including cache strategies, BFF refactoring, database sharding, and consistency mechanisms—to ensure scalability and reliability.
This article explores the evolution of security alert automation from manual verification to SOAR and AI-driven solutions, detailing MCP-based AI agents, integration with various security tools, practical case studies of honey‑pot, HIDS, and EDR alert tracing, and the resulting efficiency gains and future outlook.
RIVAL (Reinforcement Learning with Iterative and Adversarial Optimization) introduces an adversarial game between a reward model and a translation LLM, combining qualitative preference rewards with quantitative metrics like BLEU, to overcome distribution shift in RLHF and achieve superior performance on conversational subtitle and WMT translation tasks.
Bilibili’s game algorithm team built a four‑layer, LLM‑based translation platform that automates terminology extraction, retrieval‑augmented generation, and quality assessment, dramatically reducing localization cycles by over 85% and costs by up to 80% while supporting ten languages and ensuring consistent, culturally‑accurate game text.
This article details how Bilibili’s multimedia lab leveraged a multimodal training pipeline combining data‑compressed SFT and the GRPO reinforcement‑learning algorithm to achieve a 13.5% metric boost and secure second place in the ICCV MIPI Detailed Image Quality Assessment competition.
VibeCut introduces a novel Orchestrator‑Executor dual‑agent framework for WebCut, leveraging large language models, shared structured context, and modular tool integration to automate complex video editing tasks, demonstrating improved efficiency, transparency, and adaptability across diverse scenarios while addressing challenges of multi‑agent coordination.
This article explains how Retrieval‑Augmented Generation (RAG) combines large language models with vector databases to let non‑technical staff query massive membership data using plain language, detailing the workflow, technical architecture, optimization challenges, and real‑world impact on data‑driven decision making.
TextFlux introduces an OCR‑free diffusion‑based framework that seamlessly inserts multilingual text into real‑world images using only glyph images and minimal training data, offering high visual fidelity, zero‑shot character rendering, and efficient multi‑line and single‑line generation on consumer GPUs.
This article details a deep engineering effort that transforms Kotlin Multiplatform on iOS into a first‑class development environment, covering the current pain points, root causes, a redesigned Bazel‑based build pipeline, parallel compilation techniques, performance gains, and future roadmap for mobile developers.
